Search Results (1,318)

Reconfigurable intelligent surface (RIS) emerges as a promising paradigm and offers a new perspective for physical layer security. In practice, imperfect eavesdropper channel state information (CSI) represents a critical challenge for RIS-aided physical layer security design. To tackle this issue, this paper investigates RIS-aided physical layer security enhancement under imperfect eavesdropper CSI and formulates a robust weighted sum secrecy rate maximization problem. To efficiently solve this problem, a model-driven deep learning approach is proposed. We begin by introducing the gradient descent–ascent algorithm to solve the optimization problem. Then we unfold this algorithm into a gated recurrent unit (GRU)-aided deep unfold network with trainable parameters. The proposed GRU-aided deep unfold network leverages GRU to adaptively generate gradient ascent–descent step sizes. Different from the existing deep unfold network that commonly has a fixed number of iteration, the proposed deep unfold network integrates the sequential learning capability of GRU and enables adaptive iteration adjustment. The simulation results demonstrate that compared to existing non-robust optimization algorithm and traditional deep unfold network with fixed number of iteration, the proposed method exhibits robustness against imperfect CSI and achieves higher weighted sum secrecy rate. Full article

Accurate identification of disease-associated microRNAs (miRNAs) is crucial for elucidating pathogenic mechanisms and advancing therapeutic discovery. Although computational methods, particularly those based on biological networks, have become essential tools for predicting miRNA-disease associations, existing approaches often struggle to comprehensively learn from heterogeneous data and optimize feature representations. To overcome these limitations, we propose the Multi-view Hybrid Attention Graph Convolutional Network (MV-HAGCN). This framework constructs a comprehensive heterogeneous network by integrating multi-source biological information, simultaneously capturing miRNA similarity and disease similarity. We design a hierarchical attention mechanism to enable refined feature learning: first, the Efficient Channel Attention (ECA) module prioritizes information-rich input features, ensuring the model focuses on high-value biological characteristics. Subsequently, the Multi-Head Self-Attention Graph Convolutional Network operates on these refined features. Through iterative message passing and multi-head self-attention, it captures not only direct first-order relationships between nodes but also explicitly models and infers complex, indirect higher-order relationships within the network. This hierarchical design progressively refines feature representations, from channel-level recalibration to global structural dependency modeling, enabling the model to capture both local and high-order relational patterns. Furthermore, a dynamic weight learning strategy adaptively integrates multi-perspective similarity matrices, achieving superior feature complementarity and synergy. Finally, the high-order node representations learned through multi-layer graph convolutions are fed into a multi-layer perceptron for integration and nonlinear transformation, enabling precise prediction of potential miRNA-disease associations. Comprehensive evaluation through five-fold cross-validation on HMDD v2.0 and v3.2 benchmark datasets demonstrates that MV-HAGCN consistently outperforms existing state-of-the-art methods in predictive performance. Case studies targeting key diseases such as breast cancer, lung tumors, and pancreatic disorders revealed that the top 50 miRNAs associated with each of these three conditions were all validated in databases, confirming the practical value of this model in screening candidate miRNAs with high biological relevance. Full article

Low-orbit thermal infrared bidirectional whisk-broom imaging offers wide-swath coverage and high spatial resolution for monitoring moving targets such as aircraft, but large scan angles and terrain undulation cause non-rigid geometric distortion and radiometric inconsistency between forward and backward scans. These effects generate strong clutter in difference images and degrade small and weak target detection. To address this problem, we propose an infrared small target detection method that fuses accurate registration and weighted difference. First, we propose a hybrid multi-scale registration algorithm that achieves coarse affine registration through sparse feature–point matching and then iteratively corrects nonlinear deformations by integrating a global grayscale-driven force with a local sparse-feature-guided force, yielding a registration error of 0.3281 pixels. On this basis, a multi-scale weighted convolutional morphological difference algorithm is proposed. A novel dual-structure hollow top-hat transform is constructed to accurately estimate the background, and a multi-directional convolution mechanism is introduced to effectively suppress anisotropic edge clutter and enhance target saliency. Experiments on SDGSAT-1 thermal infrared bidirectional whisk-broom data show an SCRG of 18.27, and a detection rate of 91.2% when the false alarm rate is below 0.15%. The method outperforms representative competing algorithms and provides a useful reference for space-based aerial moving target detection. Full article

Generalized incommensurate fractional differential systems (GIFDSs) unify classical fractional frameworks via weight functions, capturing non-uniform multicomponent system dynamics. This paper fills a critical research gap by analyzing GIFDSs for both commensurate and incommensurate weight functions. For commensurate weights ($w_i\left(t\right)=w\left(t\right)$), classical IFDS equivalence is established via state transformation. Linear homogeneous mild solutions are derived using the incommensurate Mittag–Leffler function. Existence and uniqueness of nonlinear solutions are proved under continuity and Lipschitz assumptions. Hyers–Ulam stability is verified for linear non-homogeneous systems. For incommensurate weights (distinct $w_i\left(t\right))$, a novel framework is developed: by the integral bound lemma and Picard iteration, local existence (existence on $[a,t_1]$) is established, then it is extended to the full interval. The global uniqueness is obtained by Gronwall-type inequality via combined substitution. These results are applied to Hopfield Neural Networks, showing that one-layer HNNs with tanh or sigmoid activations admit unique mild solutions under GIFDS dynamics. Full article

Traditional evaluations of revetment projects primarily focus on structural safety and economic analysis, which cannot comprehensively reflect the overall effectiveness of such projects. To address this issue, this paper establishes a comprehensive evaluation index system for ecological revetments based on ecosystem theory and sustainable development principles. The system is tailored for the Pinglu Canal Ecological Revetment Demonstration Project. It assesses three key aspects: structural stability, ecological health, and socioeconomic benefits. Subjective weights were calculated using the Fuzzy Analytic Hierarchy Process (FAHP). Objective weights were determined by optimizing the Projection Pursuit (PP) model with the Tent-improved Crocodile Ambush Optimization Algorithm (TCAOA). Game theory was employed to compute the combined weights. The evaluation grade of the ecological revetment project was subsequently determined using a cloud model. The results show that the cloud eigenvalues of the project’s comprehensive evaluation are (1.096, 0.209, 0.047), and the application effectiveness is rated as “Excellent”. The cloud expected values for structural stability, ecological health, and socioeconomic benefits are 1.02, 1.18, and 1.15, respectively. All of these values are at the “Excellent” level. Compared with GA-PP and PSO-PP, TCAOA-PP converges faster and more stably. It requires only 347 iterations, achieves a coefficient variation of 3.8%, and reduces computation time by 23%. By revealing the nonlinear coupling relationships among indicators, the model presented in this paper provides a methodological foundation for establishing an evaluation framework that is ecologically interpretable for bank protection. This study has important practical significance for promoting the high-quality development of inland waterways and the construction of ecological revetments. Full article

High-speed capability is a defining feature of next-generation helicopters, enabling time-sensitive missions. This paper compares three high-speed configurations: tiltrotor, coaxial rigid rotor, and compound conventional rotor. Based on existing technology and operational needs, the study focuses on the aerodynamic layout of a compound conventional rotor high-speed unmanned helicopter. With key parameters, including a 300 kg takeoff weight and a maximum speed of 240 km/h, iterative optimization was conducted using theoretical analysis, numerical simulation, and flight dynamics evaluation. A feasible aerodynamic layout based on a “dual-side propulsion concept” was developed, followed by flight performance assessment and full-scale prototype flight tests. The results show: (1) the final layout comprises a two-blade hingeless rotor, three-blade pusher propellers, wings, skid landing gear, an H-tail, and a horizontal stabilizer; (2) flight performance meets all design targets, achieving maximum and cruise speeds of 260.48 km/h and 180 km/h at 1500 m altitude; and (3) full-scale prototype tests confirm the rationality of the aerodynamic layout and the reliability of the design process, achieving a high-speed flight of 242.6 km/h at an altitude of 1280 m. This work provides a valuable configuration reference for high-speed unmanned helicopter development. Full article

Deep learning (DL) methods have achieved promising performance in infrared weak target segmentation. However, their interpretability and robustness against cluttered backgrounds and noise remain limited. We propose an adaptive-weighted deep unfolding network (AwDuNet) that unfolds alternating direction method of multipliers (ADMM) iterations for adaptive sparse–low-rank decomposition into multi-stage interpretable modules for end-to-end training. An adaptive weight matrix is jointly estimated from a local structural-difference matrix and a sparse-enhancement matrix, thereby strengthening target–background separation while preserving fine target details. To suppress background clutter, we design a dual-path complementary attention (DCA) mechanism for the low-rank background reconstruction module (LBRM), which improves low-rank background modeling by jointly leveraging spatial and channel attention. By extracting local details and global context in parallel, DCA enhances weak-target responses and mitigates interference from complex backgrounds. We also build a real-scene infrared dataset with 632 images for out-of-domain evaluation. The model is tested without fine-tuning after training on public datasets to assess practical robustness. Experiments on multiple public datasets validate the effectiveness and generalization of AwDuNet. Full article

With the explosive growth of the Internet of Things (IoT) and cloud-computing traffic, Intrusion Detection Systems (IDSs) have become a cornerstone of network security. However, modern traffic data often exhibits extreme class imbalance and long-tailed distributions, leading to persistently high miss rates for minority attack categories in Machine Learning (ML)-based IDSs. Conventional oversampling may introduce decision noise, whereas standard Generative Adversarial Networks (GANs) can suffer from training instability and mode collapse when modeling high-dimensional tabular traffic features. To address these challenges, we propose a high-fidelity traffic augmentation framework based on an Adaptive Hybrid-loss Conditional GAN (AH-CGAN). Specifically, AH-CGAN introduces an iteration-dependent adaptive gradient penalty (AGP) schedule to enforce the Lipschitz continuity constraint more effectively during training and incorporates a feature-matching objective to align intermediate critic representations between real and synthetic traffic. Experiments on the CIC-IDS2017 benchmark show that AH-CGAN generates distribution-consistent synthetic samples and that augmentation improves downstream detection across multiple classifiers. In particular, the weighted F1-score of Logistic Regression increases from 0.8237 to 0.8697 (Δ = +0.0460, i.e., +4.6%). Overall, the proposed approach enhances minority coverage in the feature space and can improve class separability, providing a practical solution for long-tailed IDS. Full article

Feature selection is essential for high-dimensional multi-source feature analysis, particularly in Internet of Things (IoT) environments characterized by data heterogeneity, redundancy, and noise. To address the need to balance classification performance, dimensionality reduction, and selection stability, this study proposes a residual-based conditional mutual information and feedback fusion (RCMF) feature-selection method. Inspired by the idea of conditional mutual information, the proposed method first introduces a residual-based indicator to characterize the incremental discriminative information retained by a candidate feature under given conditional constraints. On this basis, model-driven predictive contribution and stability score are further incorporated, and the weights of different evaluation components are iteratively updated during the feature-selection process to achieve adaptive fusion. In this way, the method jointly considers conditional discriminative information, task relevance, and selection consistency within a unified feature-evaluation procedure. Experiments on multiple publicly available benchmark and IoT-related datasets validate the rationality and effectiveness of the proposed method. Full article

Baseline correction of Raman spectra is a critical step for achieving high-precision quantitative analysis. However, the presence of complex background noise, nonlinear baseline drift, and spectral peak distortion due to peak overlap in real spectral data severely limits the performance of conventional correction methods. To better preserve spectral details, this study proposes an improved penalized least squares method for Raman spectral baseline correction. Compared with common baseline correction approaches, the proposed method optimizes the iterative weight function through precise noise classification, significantly enhancing the algorithm’s flexibility. The traditional single smoothing parameter is extended into a smoothing vector, and a classification strategy consistent with that of the penalty parameter is adopted, enabling synchronous optimization and coordinated adjustment of both during iteration. Furthermore, based on the physical constraints of Raman spectra, the algorithm eliminates non-physical solutions that may arise in traditional iterative processes, ensuring the fidelity of the corrected spectra. Experimental results demonstrate that the proposed method exhibits strong robustness under various noise conditions and significantly improves correction accuracy. Full article

Groundwater sustains human activity in arid crystalline terrains where surface water is scarce and hydrogeological data are limited. However, most groundwater potential mapping approaches depend on deterministic weighting methods without quantifying model variability. This study describes an uncertainty-aware Remote Sensing and Geographic Information Systems (RS–GIS) framework to delineate groundwater potential zones in the Wadi Arab Watershed, Northeastern Sudan. Nine thematic factors—geology and lithology, rainfall, slope, drainage density, lineament density, soil, land use/land cover, topographic wetness index, and height above nearest drainage—were integrated using the Analytical Hierarchy Process (AHP), with acceptable consistency (Consistency Ratio (CR) < 0.1). To address subjectivity in weights, a Dirichlet-based Monte Carlo simulation (500 iterations) was implemented to perturb AHP weights whilst preserving compositional constraints. The resulting Groundwater Potential Index (GWPI) classified 32.69% of the watershed as high to very high potential, primarily associated with alluvial deposits and fractured crystalline rocks. Model validation using Receiver Operating Characteristic (ROC) analysis yielded an Area Under the Curve (AUC) of 0.704, indicating acceptable predictive performance. Uncertainty assessment showed low spatial variability (mean standard deviation (SD) = 0.215) and stable exceedance probabilities, verifying the robustness of predicted high-potential zones. The proposed probabilistic AHP framework augments decision reliability and provides a transferable, cost-effective tool for groundwater planning in data-limited arid basement environments. Full article

Patients leave millions of medical reviews annually, providing critical data for quality management. However, manual processing is infeasible, and existing systems fail to distinguish medical from organizational problems—a distinction essential for complaint routing. The consequences of misrouting are significant: clinical issues may go unaddressed when medical complaints reach administrative staff, while systemic service problems remain unresolved when organizational complaints reach medical directors. We developed a hybrid approach combining expert annotation with Large Language Models (LLMs). Fifteen prompt iterations on 1500 reviews with expert validation (modified Cohen’s kappa (κ_mod), which weights errors hierarchically, reached 0.745) preceded the LLM annotation of 15,000 mixed-sentiment and positive reviews. These were combined with 7417 expert-annotated negative reviews to form a corpus of 22,417 reviews. Eight architectures, ranging from Logistic Regression to a BERT + TF-IDF + LightGBM ensemble, were compared using both standard metrics and domain-specific practical metrics tailored to complaint routing. The best model, scaled to 4.3 million Russian-language reviews from the Prodoctorov.ru platform, achieved 92.9% Practical Accuracy—the proportion of reviews classified without critical medical–organizational misclassification errors (M ↔ O)—compared to 68.0% standard accuracy, which treats all errors equally. Critical errors were reduced to 1.4%, yielding 144,000 more correctly processed complaints than traditional methods (TF-IDF + Logistic Regression). Analysis of the scaled data revealed the following: 46.1% M (medical), 21.0% O (organizational), and 32.9% C (combined) reviews; medical ratings were highest (4.75 vs. 4.59 for organizational, p < 0.001); combined reviews were longest (802 characters); zero-star reviews comprised 3.8% of feedback, with organizational complaints dominating (38.2%) among extreme negatives; and average ratings rose by 1.24 points over 14 years. This hybrid approach yields expert-comparable corpora, automates 93% of feedback processing, ensures correct complaint routing, and contributes to healthcare sustainability by reducing administrative burden, accelerating resolution, and enabling data-driven quality management without proportional increases in human resources. All analyses were conducted on Russian-language patient reviews. Full article

Uniform circular arrays (UCAs) are widely used in underwater source localization due to their omnidirectional coverage. However, random sensor position errors caused by installation inaccuracies and environmental disturbances convert UCAs into non-uniform circular arrays (NCAs), severely degrading the performance of high-resolution direction of arrival (DOA) estimation algorithms. To address this issue, this paper proposes a robust DOA estimation method that integrates empirical mode decomposition (EMD) denoising with prior-weighted iterative least squares (PWLS) correction. The method first applies EMD to adaptively denoise received signals by selecting intrinsic mode functions based on a combined energy-correlation criterion. An initial DOA estimate is then obtained using the MUSIC algorithm. Finally, a PWLS correction algorithm leverages prior knowledge of deviated sensors to iteratively fit the circle center and gradually pull sensor positions toward the ideal circumference, using a differentiated relaxation mechanism to suppress outliers while preserving geometric features. Systematic Monte Carlo simulations compare five correction algorithms under multi-frequency and wideband signals. The results show that both multi-frequency and wideband signals reduce estimation errors to below 0.1°, with the proposed PWLS achieving the best accuracy under multi-frequency signals, while all algorithms approach zero error under wideband signals. The PWLS algorithm converges in about 10 iterations with high computational efficiency, providing a reliable solution for practical underwater NCA applications. Full article

Weight reduction and dynamic performance optimization are critical for airborne direct-drive VICTS satellite communication antennas, which require lightweight, high-speed, and high-precision rotation. Traditional vibration suppression methods, such as uniform support layout and added damping, rely heavily on empirical trial and error, lack targeted modal control, and cannot balance lightweight design with dynamic stiffness. To address these issues, this paper proposes a wave-theory-based dynamic modeling and rapid optimization method for multi-layer rotating components in direct-drive VICTS antennas. The kinematic model of the rotating ring and ball revolution excitation are derived using the annular wave equation and bearing kinematics. A Modal Blocking Mechanism is established: placing support balls at positions satisfying the half-wavelength constraint suppresses target mode shapes via wave interference, achieving vibration attenuation at the source. A homogenization equivalent method based on RVE is developed for irregular cross-section rings, yielding analytical expressions for in-plane equivalent elastic modulus and out-of-plane equivalent shear modulus. These parameters are integrated into the wave equation to analytically solve vibration modes, avoiding iterative finite element computations. A rapid multi-objective optimization framework is then constructed, minimizing the structural weight and maximizing the modal separation interval under dynamic stiffness and excitation frequency constraints. Numerical simulations, FE analysis, and prototype tests validate the method: the maximum analytical error is only 3.1%. Compared with uniform support designs, the optimized structure achieves a 40% weight reduction, a 40% increase in minimum modal separation, and a 65% reduction in the RMS tracking error. This work provides an efficient, deterministic dynamic design method for large-diameter ring structures, transforming vibration control from empirical adjustment into a precise, physics-informed optimization. Full article

Background: Transient ischemic attack (TIA) and minor stroke often result in excellent functional recovery but are frequently followed by substantial psychological morbidity. It remains unclear whether mood disturbances or cognitive impairment are the primary contributors to reduced health-related quality of life (HRQoL) in this population. Methods: We conducted a prospective observational case–control study including 90 patients with acute TIA or minor stroke confirmed by diffusion-weighted imaging and 92 age-matched healthy controls. At 90 days, participants completed the Hamilton Depression Rating Scale, Hamilton Anxiety Rating Scale, Montreal Cognitive Assessment, and the EQ-5D-5L. Hierarchical multiple regression using standardized z-scores identified independent predictors of HRQoL. Bias-corrected bootstrapped mediation analyses (5000 iterations) assessed whether cognitive impairment mediated the relationship between mood symptoms and HRQoL. Results: Compared with controls, patients exhibited markedly higher rates of depressive symptoms (82.2% vs. 18.5%), anxiety symptoms (81.1% vs. 21.7%), and cognitive impairment (66.7% vs. 13.0%) (all p < 0.001). Psychopathological variables explained an additional 36.6% of HRQoL variance, whereas cognitive and neuroimaging variables contributed only 1.7% (ΔR² = 0.017; p = 0.523). In the fully adjusted regression model, HAM-A showed the numerically largest standardized coefficient (β = −0.055; p = 0.064), representing a trend toward significance, while HDRS-17 did not individually reach statistical significance (β = −0.043; p = 0.147); cognitive impairment had negligible independent effects (β = −0.001; p = 0.947). Both mood variables collectively accounted for the substantial majority of explained HRQoL variance, far exceeding the contribution of cognitive and neuroimaging predictors. Mediation analyses revealed no significant indirect effects, indicating that mood and cognitive complications are statistically consistent with a model in which mood and cognitive symptoms exert independent effects on HRQoL; temporal ordering cannot be established from these cross-sectional measures. Conclusions: Following TIA or minor stroke, depressive and anxiety symptoms are highly prevalent, persist despite good neurological recovery, and exert a disproportionately negative impact on HRQoL. Anxiety appears particularly influential in determining patient-reported outcomes. The statistical consistency of the mediation models with parallel rather than sequential mood–cognition pathways suggests that these represent independent neurobiological sequelae requiring separate clinical attention, underscoring the need for routine and concurrent assessment of both mood and cognitive function after TIA and minor stroke. Full article